MétaCan
Menu
Back to cohort
Record W3213414280 · doi:10.29173/mocs175

BIM-enabled Modular and Industrialized Construction in China

2015· article· en· W3213414280 on OpenAlexvenueno aff
Peining Hu, Jinyue Zhang

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsIndustrialisationChinaModular designProductivitySustainable developmentBusinessQuality (philosophy)Resource (disambiguation)Industry of ChinaEngineeringIndustrial organizationEconomic growthComputer scienceEconomicsPolitical science

Abstract

fetched live from OpenAlex

The Chinese construction market is huge in terms of its size but facing many issues not matching its high speed development, such as low field productivity, unreliable quality, high resource and energy consumption, often safety accidents, and significant environment pollution. Those issues are mainly associated with the old fashion of construction method. The concept of industrialization of construction has been recognized since the establishment of the People’s Republic of China, but did not get well developed until recently the industry is under the pressure of increasing labour cost and the demand of sustainable development. There was a surge of Building Information Modeling (BIM) application in last few years in China and the industry has seen lots of benefits of virtual design and construction. Integrating BIM technology into industrialization of construction is seen as a promising opportunity to improve the performance of modular and industrialized construction. This paper first reviews the history of industrialization of the Chinese construction industry and then discusses the recent BIM adoption in China. The approaches of using BIM to enable modular design and industrialized construction and installation in China are the main focus of this paper.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.195
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueModular and Offsite Construction (MOC) Summit ProceedingsSame topicBIM and Construction IntegrationFrench-language works237,207